Metadata-Version: 2.3
Name: mlgear
Version: 0.5
Summary: Utility scripts for machine learning
License: MIT
Author: Peter Hurford
Author-email: peter@peterhurford.com
Requires-Python: >=3.7,<4.0
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Dist: keras
Requires-Dist: lightgbm
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: scikit-learn
Project-URL: Repository, https://github.com/peterhurford/mlgear
Description-Content-Type: text/markdown

## MLGear

Some utility functions to make ML with Python / Pandas / sklearn even easier

### Example Usage

```Python
from mlgear.cv import run_cv_model
from mlgear.models import runLGB
from mlgear.metrics import rmse

lgb_params = {'application': 'regression',
              'boosting': 'gbdt',
              'metric': 'rmse',
              'num_leaves': 15,
              'learning_rate': 0.01,
              'bagging_fraction': 0.9,
              'feature_fraction': 0.9,
              'verbosity': -1,
              'seed': 1,
              'lambda_l1': 1,
              'lambda_l2': 1,
              'early_stop': 20,
              'verbose_eval': 10,
              'num_rounds': 500,
              'num_threads': 3}

results = run_cv_model(train, test, target, runLGB, lgb_params, rmse)
```

### Installation

```
pip install mlgear
```

For development:

```
# Install poetry if you don't have it
pip install poetry

# Install dependencies
poetry install

# Build the package
poetry build

# Publish to PyPI
poetry publish
```

